Machine learning approaches for integrating clinical and imaging features in late-life depression classification and response prediction

International Journal of Geriatric Psychiatry

Volumn 30, Issue 10, 2015, Pages 1056-1067

  Patel, Meenal J ^a   Andreescu, Carmen ^b   Price, Julie C ^b   Edelman, Kathryn L ^b   Reynolds, Charles F ^b   Aizenstein, Howard J ^a,b  

^a University of Pittsburgh   (United States)

^b UNIVERSITY OF PITTSBURGH SCHOOL OF MEDICINE   (United States)

Author keywords

diagnosis; imaging; late life depression; learning; prediction; treatment response

Indexed keywords

AGE; AGED; ALTERNATING DECISION TREE; ARTICLE; BRAIN ATROPHY; CLASSIFICATION; CLINICAL ARTICLE; COGNITION; CONTROLLED STUDY; DIFFUSION TENSOR IMAGING; FEMALE; FUNCTIONAL MAGNETIC RESONANCE IMAGING; HUMAN; LATE LIFE DEPRESSION; LINEAR KERNEL; MACHINE LEARNING; MALE; MINI MENTAL STATE EXAMINATION; NUCLEAR MAGNETIC RESONANCE IMAGING; PREDICTION; RADIAL BASIS FUNCTION KERNEL; REGULARIZED LOGISTIC REGRESSION; SUPPORT VECTOR MACHINE; TREATMENT RESPONSE; WHITE MATTER; ADULT; BRAIN MAPPING; DEPRESSIVE DISORDER; IMAGE PROCESSING; MIDDLE AGED; NERVE TRACT; NEUROIMAGING; NEUROPSYCHOLOGICAL TEST; ONSET AGE; PATHOLOGY; PREDICTIVE VALUE; PROCEDURES;

ADULT; AGE OF ONSET; AGED; BRAIN MAPPING; DEPRESSIVE DISORDER; FEMALE; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MACHINE LEARNING; MAGNETIC RESONANCE IMAGING; MALE; MIDDLE AGED; NEURAL PATHWAYS; NEUROIMAGING; NEUROPSYCHOLOGICAL TESTS; PREDICTIVE VALUE OF TESTS;

EID: 84941802558     PISSN: 08856230     EISSN: 10991166     Source Type: Journal    
DOI: 10.1002/gps.4262     Document Type: Article

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